AI chips, also known as AI accelerators or specialized processors, are hardware designed to efficiently handle artificial intelligence workloads such as training and inference for machine learning models. These include graphics processing units (GPUs), tensor processing units (TPUs), and custom application-specific integrated circuits (ASICs). By 2026, the AI chip market has exploded due to the generative AI boom, with global semiconductor sales projected to surpass $1 trillion annually, driven largely by AI accelerators representing a $900 billion opportunity.[2] NVIDIA remains the dominant player, but competition is intensifying from established firms like AMD and Intel, cloud providers developing custom chips, and startups focusing on efficiency and inference.[9] This report covers major categories of AI chips, key manufacturers, their flagship products, market trends, and future outlook as of January 2026.
AI chips are broadly categorized based on their application environments:
The market is shifting from general-purpose GPUs toward custom ASICs for better efficiency in inference, as LLMs reach plateaus in scale and focus on "tokens per watt."[6]
Below is a comprehensive overview of key AI chip makers, drawn from industry analyses.[9][3] The tables highlight selected chips, categories, and notable details.
These focus on high-performance computing for AI training and inference in data centers.
| Vendor | Category | Selected AI Chip | Key Details |
|---|---|---|---|
| NVIDIA | Leading producer | Blackwell Ultra | Revenue leader; powers DGX systems like H100/H200/B300; dominant in training; software ecosystem (CUDA) gives edge.[9] |
| AMD | Leading producer | MI400 (MI300 for training, MI325X for inference) | Second in market valuation; acquired teams for inference optimization; competes on cost-effectiveness.[9] |
| Intel | Leading producer | Gaudi 3 | CPU giant catching up in GPUs; uses own foundry; focuses on enterprise solutions.[9] |
| AWS (Amazon) | Public cloud & chip producer | Trainium3 (Trainium2 for clusters) | Powers Anthropic's models; emphasizes model training efficiency.[9] |
| GCP (Google) | Public cloud & chip producer | Ironwood (Trillium as 6th gen TPU) | Strong in LLMs and parallel processing; 2x power efficiency over prior gens.[9] |
| Alibaba | Public cloud & chip producer | ACCEL (Hanguang 800 for inference) | Developed with Chinese partners; geopolitical considerations for adoption.[9] |
| IBM | Public cloud & chip producer | NorthPole (AIU on Telum Processor) | Integrates with watson.x; focuses on fraud detection and compute-memory fusion.[9][3] |
| Huawei | Public cloud & chip producer | Ascend 920 (910 family) | ~60% of NVIDIA H100 performance; used in China amid sanctions.[9] |
| Groq | Public AI cloud & chip producer | LPU Inference Engine (GroqChip) | LLM inference specialist; $1.5B funding; 70k developers on platform.[9] |
| SambaNova Systems | Public AI cloud & chip producer | SN40L | High-performance for generative AI; promotes sustainable practices.[9] |
For a performance comparison of top datacenter chips, see the interactive chart below:
[](grok_render_citation_card_json={"cardIds":["823f1f","71930f"]})These are system-on-chips (SoCs) for smartphones and tablets, enabling on-device AI like image processing and voice recognition.
| Vendor | Selected AI Chip | Used In | Key Details |
|---|---|---|---|
| Apple | A18 Pro, A18 | iPhone 16 series | Focuses on integrated neural engines for privacy-focused AI.[9] |
| Huawei | Kirin 9000S | Mate 60 series | Advanced NPU for photography and AI tasks; China-centric.[9] |
| MediaTek | Dimensity 9400, 9300 Plus | Oppo Find X8, Vivo X200, Samsung Galaxy Tab S10 | Affordable high-performance for mid-to-high-end devices.[9] |
| Qualcomm | Snapdragon 8 Elite (Gen 4), 8 Gen 3 | Samsung Galaxy S25/S24, Xiaomi 14, OnePlus 12 | Edge AI leader; strong in 5G-integrated AI processing.[9][3] |
| Samsung | Exynos 2400, 2400e | Galaxy series | Custom designs for Samsung ecosystem; improving AI capabilities.[9] |
Low-power chips for decentralized AI in devices like drones and sensors.
| Vendor | Selected AI Chip | Performance (TOPS) | Power (W) | Applications |
|---|---|---|---|---|
| NVIDIA | Jetson Orin | 275 | 10-60 | Robotics, autonomous systems.[9] |
| Edge TPU | 4 | 2 | IoT, embedded systems.[9] | |
| Intel | Movidius Myriad X | 4 | 5 | Drones, cameras, AR devices.[9] |
| Hailo | Hailo-8 | 26 | 2.5-3 | Smart cameras, automotive.[9] |
| Qualcomm | Cloud AI 100 Pro | 400 | Varies | Mobile AI, autonomous vehicles.[9] |
Startups are innovating in niche areas like wafer-scale engines and energy-efficient designs.[9][3]
Upcoming producers include:
Other notable firms: Graphcore (IPU-POD256, acquired by Softbank), Mythic (analog edge compute), Speedata (APU for big data), Axelera AI (Titania chiplet).
Chinese players like Cambricon, Baidu (Kunlun 3rd gen), Biren (BR106/110), and Moore Threads (MTT S2000) are advancing domestically amid US sanctions.[9][1]
The AI chip sector is at the midpoint of a decade-long transformation, with a 30% year-over-year sales surge expected in 2026.[2] Key trends:
The following interactive chart illustrates the estimated market share distribution:
[](grok_render_citation_card_json={"cardIds":["d22b0d","b81757","395574","3716db"]})Market size projections are shown in the chart below:
[](grok_render_citation_card_json={"cardIds":["34c297","e5d54b"]})By 2030, the AI data center market could reach $1.2 trillion, growing at 38% CAGR.[2] Advances will focus on system-level optimizations (e.g., rack-scale supercomputers) rather than individual chips.[8] Geopolitical tensions may accelerate diversified supply chains, while startups innovate in energy-efficient and specialized architectures. Overall, the sector promises continued growth, with inference overtaking training in market size.[9]
AI chips are the backbone of modern AI, with NVIDIA setting the pace while a diverse ecosystem of competitors drives innovation. As of 2026, the market is vibrant, blending established giants with agile startups to meet escalating demands for efficient, scalable AI hardware.